Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study
BackgroundAccurate identification of pathologic grade before operation is helpful for guiding clinical treatment decisions and improving the prognosis for soft tissue sarcoma (STS).PurposeTo construct and assess a magnetic resonance imaging (MRI)-based radiomics nomogram incorporating intratumoral h...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1433196/full |
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| author | Bo Wang Hongwei Guo Meng Zhang Yonghua Huang Lisha Duan Chencui Huang Jun Xu Hexiang Wang |
| author_facet | Bo Wang Hongwei Guo Meng Zhang Yonghua Huang Lisha Duan Chencui Huang Jun Xu Hexiang Wang |
| author_sort | Bo Wang |
| collection | DOAJ |
| description | BackgroundAccurate identification of pathologic grade before operation is helpful for guiding clinical treatment decisions and improving the prognosis for soft tissue sarcoma (STS).PurposeTo construct and assess a magnetic resonance imaging (MRI)-based radiomics nomogram incorporating intratumoral habitats (subregions of clusters of voxels containing similar features) and peritumoral features for the preoperative prediction of the pathological grade of STS.MethodsThe MRI data of 145 patients with STS (74 low-grade and 71 high-grade) from 4 hospitals were retrospectively collected, including enhanced T1-weighted and fat-suppressed-T2-weighted sequences. The patients were divided into training cohort (n = 102) and validation cohort (n = 43). K-means clustering was used to divide intratumoral voxels into three habitats according to signal intensity. A number of radiomics features were extracted from tumor-related regions to construct radiomics prediction signatures for seven subgroups. Logistic regression analysis identified peritumoral edema as an independent risk factor. A nomogram was created by merging the best radiomics signature with the peritumoral edema. We evaluated the performance and clinical value of the model using area under the curve (AUC), calibration curves, and decision curve analysis.ResultsA multi-layer perceptron classifier model based on intratumoral habitats and peritumoral features combined gave the best radiomics signature, with an AUC of 0.856 for the validation cohort. The AUC of the nomogram in the validation cohort was 0.868, which was superior to the radiomics signature and the clinical model established by peritumoral edema. The calibration curves and decision curve analyses revealed good calibration and a high clinical application value for this nomogram.ConclusionThe MRI-based nomogram is accurate and effective for predicting preoperative grading in patients with STS. |
| format | Article |
| id | doaj-art-6234633f259b4dc1b3635f8d6d4274eb |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-6234633f259b4dc1b3635f8d6d4274eb2025-08-20T02:50:07ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2024-12-011410.3389/fonc.2024.14331961433196Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary studyBo Wang0Hongwei Guo1Meng Zhang2Yonghua Huang3Lisha Duan4Chencui Huang5Jun Xu6Hexiang Wang7Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, ChinaDepartment of Operation Center, Women and Children’s Hospital, Qingdao University, Qingdao, Shandong, ChinaDepartment of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, ChinaDepartment of Radiology, The Puyang Oilfield General Hospital, Puyang, Henan, ChinaDepartment of Radiology, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaDepartment of Research Collaboration, Research and Development (R&D) Center, Beijing Deepwise and League of Philosophy Doctor (PHD) Technology Co., Ltd, Beijing, ChinaDepartment of Radiology, Peking University Third Hospital, Beijing, ChinaDepartment of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, ChinaBackgroundAccurate identification of pathologic grade before operation is helpful for guiding clinical treatment decisions and improving the prognosis for soft tissue sarcoma (STS).PurposeTo construct and assess a magnetic resonance imaging (MRI)-based radiomics nomogram incorporating intratumoral habitats (subregions of clusters of voxels containing similar features) and peritumoral features for the preoperative prediction of the pathological grade of STS.MethodsThe MRI data of 145 patients with STS (74 low-grade and 71 high-grade) from 4 hospitals were retrospectively collected, including enhanced T1-weighted and fat-suppressed-T2-weighted sequences. The patients were divided into training cohort (n = 102) and validation cohort (n = 43). K-means clustering was used to divide intratumoral voxels into three habitats according to signal intensity. A number of radiomics features were extracted from tumor-related regions to construct radiomics prediction signatures for seven subgroups. Logistic regression analysis identified peritumoral edema as an independent risk factor. A nomogram was created by merging the best radiomics signature with the peritumoral edema. We evaluated the performance and clinical value of the model using area under the curve (AUC), calibration curves, and decision curve analysis.ResultsA multi-layer perceptron classifier model based on intratumoral habitats and peritumoral features combined gave the best radiomics signature, with an AUC of 0.856 for the validation cohort. The AUC of the nomogram in the validation cohort was 0.868, which was superior to the radiomics signature and the clinical model established by peritumoral edema. The calibration curves and decision curve analyses revealed good calibration and a high clinical application value for this nomogram.ConclusionThe MRI-based nomogram is accurate and effective for predicting preoperative grading in patients with STS.https://www.frontiersin.org/articles/10.3389/fonc.2024.1433196/fullsoft tissue sarcomahabitatsnomogramradiomicsgrade |
| spellingShingle | Bo Wang Hongwei Guo Meng Zhang Yonghua Huang Lisha Duan Chencui Huang Jun Xu Hexiang Wang Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study Frontiers in Oncology soft tissue sarcoma habitats nomogram radiomics grade |
| title | Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study |
| title_full | Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study |
| title_fullStr | Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study |
| title_full_unstemmed | Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study |
| title_short | Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study |
| title_sort | prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram a multi center preliminary study |
| topic | soft tissue sarcoma habitats nomogram radiomics grade |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1433196/full |
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